Abstract
Recommender systems are used to suggest items to users based on their preferences. Recommender systems use a set of similarity measures as part of their mechanism that could help to identify interesting items. Even though several similarity measures have been presented in the literature, most of them consider only the rating of similar users and suffer from a range of drawbacks. In order to fix these problems, we propose a novel similarity measure based on the semantic and structural information in the network. On one hand, the preference of the target user is calculated using the similarity between similar users based on several factors such as user profile, ratings, and tags. On the other hand, a user is an element who has relations with other elements in the network. Therefore, we can use the network topology in the similarity measurement. We apply this idea in the social recommender system to improve the quality of recommendations. The experimental results show that our method achieves better precision and accuracy and handles the cold-start problem.
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El Kouni, I.B., Karoui, W., Romdhane, L.B. (2021). A Novel Structural and Semantic Similarity in Social Recommender Systems. In: Barolli, L., Yim, K., Enokido, T. (eds) Complex, Intelligent and Software Intensive Systems. CISIS 2021. Lecture Notes in Networks and Systems, vol 278. Springer, Cham. https://doi.org/10.1007/978-3-030-79725-6_3
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DOI: https://doi.org/10.1007/978-3-030-79725-6_3
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